Objective: The purpose of our study was to develop, evaluate, and deploy an automated tuberculosis detection and classification algorithm of chest radiography that can be used in international tuberculosis mass-screening and surveillance programs. The software could help countries with high tuberculosis incidence and a disproportionate number of radiologists available to curtail the spread of the disease using efficient computer-aided diagnostics.

Materials and Methods: Two radiologists identified 1671 abnormal findings in 342 chest radiographs of patients with confirmed tuberculosis from The Shenzhen No. 3 Hospital in China. They classified the findings into 17 categories (e.g., nodule, infiltrate, cavity, pleural effusion), with severities of mild, moderate, and severe. Using the Firefly annotation tool (firefly.cs.missouri.edu, University of Missouri), the radiologists annotated each chest radiograph by selecting the finding category, selecting a drawing tool that approximates the abnormal shape as closely as possible (polygon, circle, etc.), and outlining the abnormality on the chest radiograph. During this process, the radiologists applied intentional overreading techniques advocated by the World Health Organization (WHO) in its handbook on tuberculosis prevalence surveys. For each annotated abnormality and application of atlas-based lung segmentation and registration, we computed histogram-based shape and texture features, including histogram of gradients, local binary patterns, and eigenvalues. All features are concatenated into a single feature vector. Finally, we used the resulting set of feature vectors for training and testing of a linear support vector machine. We quantified the ability to detect and grade abnormalities based on cross-evaluation on the manually annotated data.

Results: Our system classified chest radiographs as either normal or abnormal with 90% AUC. Abnormalities are classified with variable accuracy; for example, infiltrates are correctly classified in 90% of the cases, and their severity is correctly graded in 87% of the cases (consistently for both radiologists). Moreover, using a feature-specific distance function, we can visualize the degree of similarity between the previously annotated regions and suspicious regions in newly presented chest radiographs for interactive computer-aided diagnostics.

Conclusion: Our pilot demonstrates potential for fully automated identification and classification of findings compatible with tuberculosis on chest radiography. Furthermore, our statistics provide clues to the frequency and common locations of tuberculosis manifestations. We intend on making the annotated images publicly available for further analysis and institutional challenges. Our automatic identification and classification of tuberculosis manifestations, per WHO recommendations, should help establish screening programs in developing regions. We will report on first tests of our portable screening system currently deploying in Africa.